{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "逻辑回归又称logistic回归分析，是一种广义的线性回归分析模型，常用于数据挖掘，疾病自动诊断，经济预测等领域。逻辑回归从本质来说属于二分类问题。\n",
    "\n",
    "二分类问题是指预测的y值只有两个取值（0或1），二分类问题可以扩展到多分类问题。例如：我们要做一个垃圾邮件过滤系统，x是邮件的特征，预测的y值就是邮件的类别，是垃圾邮件还是正常邮件。对于类别我们通常称为正类（positive class）和负类（negative class），垃圾邮件的例子中，正类就是正常邮件，负类就是垃圾邮件。\n",
    "\n",
    "在二分类的问题中经常还会遇到不平衡类的分类问题，例如在异常值检测的应用中异常值的数量要远远小于正常值的数量、在信用卡欺诈检测的应用中涉嫌欺诈的信用卡用户数量要远远小于正常用户的数量、在用户流失预测的应用中等流失用户的数量要远远小于未流失的用户数量。在这些不平衡的二分类问题中我们所关注的重点或者说我们所需要预测的往往是那些占比教小的分类，比如\n",
    "\n",
    "* 我们只需要能准确识别出哪些是异常值而不需要去关心正常值\n",
    "* 我们只需要识别出哪些是涉嫌欺诈的信用卡用户而不用去关心正常的信用卡用户\n",
    "* 我们只需要识别出哪些用将会流失而不用去关心正常用户。\n",
    "\n",
    "在这些不平衡类的分类问题中，预测的准确率(accuracy)已经不是一个很好的评估指标，因为它会忽视小类(负类)的预测准确率，但是小类(负类)往往是我们关注的重点。此时我们应该借助于其他的评估指标如：查准率、查全率、F1等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据\n",
    "\n",
    "\n",
    "我们的数据来自于葡萄牙银行机构的营销活动数据，该营销活动通过向用户打电话来推销银行的理财产品， 你可以在这里下载(http://archive.ics.uci.edu/ml/datasets/Bank+Marketing ),数据中包含了41188条银行客户信息和21个字段:\n",
    "\n",
    "* 1 - age ：客户年龄(numeric)\n",
    "* 2 - job : 工作类型 (分类型变量: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "* 3 - marital : 婚姻状态 (分类型变量: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "* 4 - education 文化程度(分类型变量: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "* 5 - default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "* 6 - housing: 是否有住房贷款 (分类型变量: 'no','yes','unknown')\n",
    "* 7 - loan: 是否有个人贷款? (分类型变量:  'no','yes','unknown')\n",
    "\n",
    "与打电话相关的变量:\n",
    "* 8 - contact: 联系类型 (分类型变量:  'cellular','telephone')\n",
    "* 9 - month: 最后一次联系的月份 (分类型变量:  'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "* 10 - day_of_week: 最后一次联系所在的星期 (分类型变量:  'mon','tue','wed','thu','fri')\n",
    "* 11 - duration: 最后一次联系的持续时间, 以秒为单位（数字）。 重要提示：此变量会严重影响输出目标变量（例如，如果持续时间= 0，则y ='否'）。 然而，在电话呼叫之前不知道持续时间。 此外，在通话结束后，显然已知y。 因此，此输入仅应包括在基准目的中，如果打算采用现实的预测模型，则应将其丢弃。\n",
    "\n",
    "其他属性:\n",
    "* 12 - campaign: 与用户电话联系的次数 (numeric, 包含最后一次联系)\n",
    "* 13 - pdays: 最后一次联系与前一次联系之间间隔的天数 (numeric; 999 表示之前从未联系过该客户)\n",
    "* 14 - previous: 在该次营销活动之前与客户联系的次数 (numeric)\n",
    "* 15 - poutcome: 上一次营销活动的结果 (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "社会和经济背景属性\n",
    "* 16 - emp.var.rate: 就业变化率 - 季度指标 (numeric)\n",
    "* 17 - cons.price.idx: 消费者价格指数 - 月度指标 (numeric)\n",
    "* 18 - cons.conf.idx: 消费信心指数 - 月度指标 (numeric)\n",
    "* 19 - euribor3m: 欧洲银行同业拆借利率3个月-每日指标(numeric)\n",
    "* 20 - nr.employed: 员工人数 - 季度指标 (numeric)\n",
    "\n",
    "输出变量 (目标变量):\n",
    "* 21 - y - 客户是否订购理财产品? (0:'no',1:'yes')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 21)\n",
      "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y']\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/banking.csv', header=0)\n",
    "df = df.dropna()\n",
    "print(df.shape)\n",
    "print(list(df.columns))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在文化程度(education)这列中包含了很多的类别，我们需要对其进行整理，我们要将其中的basic.4y,basic.6y,basic.9y这三个类别合并为basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.4y', 'unknown', 'university.degree', 'high.school',\n",
       "       'basic.9y', 'professional.course', 'basic.6y', 'illiterate'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education']=np.where(df['education'] =='basic.9y', 'Basic', df['education'])\n",
    "df['education']=np.where(df['education'] =='basic.6y', 'Basic', df['education'])\n",
    "df['education']=np.where(df['education'] =='basic.4y', 'Basic', df['education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Basic', 'unknown', 'university.degree', 'high.school',\n",
       "       'professional.course', 'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析\n",
    "\n",
    "\n",
    "y是我们的目标变量，当y为0时表示用户没有订购了银行的理财产品,当y为1表示用户订购了银行的理财产品。接下来我们要查看一下y的数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    36548\n",
      "1     4640\n",
      "Name: y, dtype: int64\n",
      "0    0.887346\n",
      "1    0.112654\n",
      "Name: y, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['y'].value_counts())\n",
    "print(df['y'].value_counts()/len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的统计结果中我们看到没有订购理财产品的人数为36548,占比为88.73%，而订购理财产品的人数为4640,占比为11.2%，很明显这是一个不平衡的二分类，未订购人数远大于订购人数,但是我们需要关注的重点却是那些占比较小的未订购人数，我们的模型需要能准确预测出未订购的客户。下面我们队目标变量进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y',data=df, palette='hls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们对目标变量进行更深入的探索："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.911185</td>\n",
       "      <td>220.844807</td>\n",
       "      <td>2.633085</td>\n",
       "      <td>984.113878</td>\n",
       "      <td>0.132374</td>\n",
       "      <td>0.248875</td>\n",
       "      <td>93.603757</td>\n",
       "      <td>-40.593097</td>\n",
       "      <td>3.811491</td>\n",
       "      <td>5176.166600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.913147</td>\n",
       "      <td>553.191164</td>\n",
       "      <td>2.051724</td>\n",
       "      <td>792.035560</td>\n",
       "      <td>0.492672</td>\n",
       "      <td>-1.233448</td>\n",
       "      <td>93.354386</td>\n",
       "      <td>-39.789784</td>\n",
       "      <td>2.123135</td>\n",
       "      <td>5095.115991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "y                                                                        \n",
       "0  39.911185  220.844807  2.633085  984.113878  0.132374      0.248875   \n",
       "1  40.913147  553.191164  2.051724  792.035560  0.492672     -1.233448   \n",
       "\n",
       "   cons_price_idx  cons_conf_idx  euribor3m  nr_employed  \n",
       "y                                                         \n",
       "0       93.603757     -40.593097   3.811491  5176.166600  \n",
       "1       93.354386     -39.789784   2.123135  5095.115991  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('y').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的结果中我们发现：\n",
    "* 购买理财商品的用户平均年龄(age)要高于未购买的用户的平均年龄\n",
    "* 购买理财商品的用户的最后一次联系的平均持续时间(duration)要明显高于未购买的用户的平均持续时间\n",
    "* 购买理财商品的用户的平均pdays(自上次联系客户以来的日子)要小于未购买的用户的平均pdays, 这是合理的，因为pdays越小，最后一次通话的日子越近，用户对理财产品的记忆越强,因此销售的机会就越大。\n",
    "* 令人奇怪的是购买理财商品的用户的平均campaign(与用户电话联系的次数)要是小于未购买的用户的平均campaign。\n",
    "\n",
    "接下来我们查看一些分类型变量如:job(职业),marita(婚姻)，education(文化程度)：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>38.187296</td>\n",
       "      <td>254.312128</td>\n",
       "      <td>2.623489</td>\n",
       "      <td>954.319229</td>\n",
       "      <td>0.189023</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>93.534054</td>\n",
       "      <td>-40.245433</td>\n",
       "      <td>3.550274</td>\n",
       "      <td>5164.125350</td>\n",
       "      <td>0.129726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>39.555760</td>\n",
       "      <td>264.542360</td>\n",
       "      <td>2.558461</td>\n",
       "      <td>985.160363</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.248995</td>\n",
       "      <td>93.656656</td>\n",
       "      <td>-41.375816</td>\n",
       "      <td>3.771996</td>\n",
       "      <td>5175.615150</td>\n",
       "      <td>0.068943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>41.723214</td>\n",
       "      <td>263.267857</td>\n",
       "      <td>2.535714</td>\n",
       "      <td>981.267170</td>\n",
       "      <td>0.138736</td>\n",
       "      <td>0.158723</td>\n",
       "      <td>93.605372</td>\n",
       "      <td>-41.283654</td>\n",
       "      <td>3.791120</td>\n",
       "      <td>5176.313530</td>\n",
       "      <td>0.085165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>45.500000</td>\n",
       "      <td>250.454717</td>\n",
       "      <td>2.639623</td>\n",
       "      <td>960.579245</td>\n",
       "      <td>0.137736</td>\n",
       "      <td>0.433396</td>\n",
       "      <td>93.676576</td>\n",
       "      <td>-39.495283</td>\n",
       "      <td>4.009645</td>\n",
       "      <td>5179.529623</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>42.362859</td>\n",
       "      <td>257.058140</td>\n",
       "      <td>2.476060</td>\n",
       "      <td>962.647059</td>\n",
       "      <td>0.185021</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>93.522755</td>\n",
       "      <td>-40.489466</td>\n",
       "      <td>3.611316</td>\n",
       "      <td>5166.650513</td>\n",
       "      <td>0.112175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>62.027326</td>\n",
       "      <td>273.712209</td>\n",
       "      <td>2.476744</td>\n",
       "      <td>897.936047</td>\n",
       "      <td>0.327326</td>\n",
       "      <td>-0.698314</td>\n",
       "      <td>93.430786</td>\n",
       "      <td>-38.573081</td>\n",
       "      <td>2.770066</td>\n",
       "      <td>5122.262151</td>\n",
       "      <td>0.252326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>39.949331</td>\n",
       "      <td>264.142153</td>\n",
       "      <td>2.660802</td>\n",
       "      <td>976.621393</td>\n",
       "      <td>0.143561</td>\n",
       "      <td>0.094159</td>\n",
       "      <td>93.559982</td>\n",
       "      <td>-40.488107</td>\n",
       "      <td>3.689376</td>\n",
       "      <td>5170.674384</td>\n",
       "      <td>0.104856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>37.926430</td>\n",
       "      <td>258.398085</td>\n",
       "      <td>2.587805</td>\n",
       "      <td>979.974049</td>\n",
       "      <td>0.154951</td>\n",
       "      <td>0.175359</td>\n",
       "      <td>93.634659</td>\n",
       "      <td>-41.290048</td>\n",
       "      <td>3.699187</td>\n",
       "      <td>5171.600126</td>\n",
       "      <td>0.081381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>25.894857</td>\n",
       "      <td>283.683429</td>\n",
       "      <td>2.104000</td>\n",
       "      <td>840.217143</td>\n",
       "      <td>0.524571</td>\n",
       "      <td>-1.408000</td>\n",
       "      <td>93.331613</td>\n",
       "      <td>-40.187543</td>\n",
       "      <td>1.884224</td>\n",
       "      <td>5085.939086</td>\n",
       "      <td>0.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>38.507638</td>\n",
       "      <td>250.232241</td>\n",
       "      <td>2.577339</td>\n",
       "      <td>964.408127</td>\n",
       "      <td>0.153789</td>\n",
       "      <td>0.274566</td>\n",
       "      <td>93.561471</td>\n",
       "      <td>-39.927569</td>\n",
       "      <td>3.820401</td>\n",
       "      <td>5175.648391</td>\n",
       "      <td>0.108260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>39.733728</td>\n",
       "      <td>249.451677</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>935.316568</td>\n",
       "      <td>0.199211</td>\n",
       "      <td>-0.111736</td>\n",
       "      <td>93.563781</td>\n",
       "      <td>-40.007594</td>\n",
       "      <td>3.466583</td>\n",
       "      <td>5157.156509</td>\n",
       "      <td>0.142012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>45.563636</td>\n",
       "      <td>239.675758</td>\n",
       "      <td>2.648485</td>\n",
       "      <td>938.727273</td>\n",
       "      <td>0.154545</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>93.718942</td>\n",
       "      <td>-38.797879</td>\n",
       "      <td>3.949033</td>\n",
       "      <td>5172.931818</td>\n",
       "      <td>0.112121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    duration  campaign       pdays  previous  \\\n",
       "job                                                                    \n",
       "admin.         38.187296  254.312128  2.623489  954.319229  0.189023   \n",
       "blue-collar    39.555760  264.542360  2.558461  985.160363  0.122542   \n",
       "entrepreneur   41.723214  263.267857  2.535714  981.267170  0.138736   \n",
       "housemaid      45.500000  250.454717  2.639623  960.579245  0.137736   \n",
       "management     42.362859  257.058140  2.476060  962.647059  0.185021   \n",
       "retired        62.027326  273.712209  2.476744  897.936047  0.327326   \n",
       "self-employed  39.949331  264.142153  2.660802  976.621393  0.143561   \n",
       "services       37.926430  258.398085  2.587805  979.974049  0.154951   \n",
       "student        25.894857  283.683429  2.104000  840.217143  0.524571   \n",
       "technician     38.507638  250.232241  2.577339  964.408127  0.153789   \n",
       "unemployed     39.733728  249.451677  2.564103  935.316568  0.199211   \n",
       "unknown        45.563636  239.675758  2.648485  938.727273  0.154545   \n",
       "\n",
       "               emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "job                                                                     \n",
       "admin.             0.015563       93.534054     -40.245433   3.550274   \n",
       "blue-collar        0.248995       93.656656     -41.375816   3.771996   \n",
       "entrepreneur       0.158723       93.605372     -41.283654   3.791120   \n",
       "housemaid          0.433396       93.676576     -39.495283   4.009645   \n",
       "management        -0.012688       93.522755     -40.489466   3.611316   \n",
       "retired           -0.698314       93.430786     -38.573081   2.770066   \n",
       "self-employed      0.094159       93.559982     -40.488107   3.689376   \n",
       "services           0.175359       93.634659     -41.290048   3.699187   \n",
       "student           -1.408000       93.331613     -40.187543   1.884224   \n",
       "technician         0.274566       93.561471     -39.927569   3.820401   \n",
       "unemployed        -0.111736       93.563781     -40.007594   3.466583   \n",
       "unknown            0.357879       93.718942     -38.797879   3.949033   \n",
       "\n",
       "               nr_employed         y  \n",
       "job                                   \n",
       "admin.         5164.125350  0.129726  \n",
       "blue-collar    5175.615150  0.068943  \n",
       "entrepreneur   5176.313530  0.085165  \n",
       "housemaid      5179.529623  0.100000  \n",
       "management     5166.650513  0.112175  \n",
       "retired        5122.262151  0.252326  \n",
       "self-employed  5170.674384  0.104856  \n",
       "services       5171.600126  0.081381  \n",
       "student        5085.939086  0.314286  \n",
       "technician     5175.648391  0.108260  \n",
       "unemployed     5157.156509  0.142012  \n",
       "unknown        5172.931818  0.112121  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('job').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>marital</th>\n",
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       "      <th>divorced</th>\n",
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       "      <td>5171.848772</td>\n",
       "      <td>0.101573</td>\n",
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       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>33.158714</td>\n",
       "      <td>261.524378</td>\n",
       "      <td>2.53380</td>\n",
       "      <td>949.909578</td>\n",
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       "    <tr>\n",
       "      <th>unknown</th>\n",
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       "      <td>312.725000</td>\n",
       "      <td>3.18750</td>\n",
       "      <td>937.100000</td>\n",
       "      <td>0.275000</td>\n",
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       "      <td>3.313038</td>\n",
       "      <td>5157.393750</td>\n",
       "      <td>0.150000</td>\n",
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      "text/plain": [
       "                age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "marital                                                                         \n",
       "divorced  44.899393  253.790330   2.61340  968.639853  0.168690      0.163985   \n",
       "married   42.307165  257.438623   2.57281  967.247673  0.155608      0.183625   \n",
       "single    33.158714  261.524378   2.53380  949.909578  0.211359     -0.167989   \n",
       "unknown   40.275000  312.725000   3.18750  937.100000  0.275000     -0.221250   \n",
       "\n",
       "          cons_price_idx  cons_conf_idx  euribor3m  nr_employed         y  \n",
       "marital                                                                    \n",
       "divorced       93.606563     -40.707069   3.715603  5170.878643  0.103209  \n",
       "married        93.597367     -40.270659   3.745832  5171.848772  0.101573  \n",
       "single         93.517300     -40.918698   3.317447  5155.199265  0.140041  \n",
       "unknown        93.471250     -40.820000   3.313038  5157.393750  0.150000  "
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('marital').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Basic</th>\n",
       "      <td>42.163910</td>\n",
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       "      <th>illiterate</th>\n",
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       "      <td>276.777778</td>\n",
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       "      <td>0.222222</td>\n",
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       "      <th>university.degree</th>\n",
       "      <td>38.879191</td>\n",
       "      <td>253.223373</td>\n",
       "      <td>2.563527</td>\n",
       "      <td>951.807692</td>\n",
       "      <td>0.192390</td>\n",
       "      <td>-0.028090</td>\n",
       "      <td>93.493466</td>\n",
       "      <td>-39.975805</td>\n",
       "      <td>3.529663</td>\n",
       "      <td>5163.226298</td>\n",
       "      <td>0.137245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>43.481225</td>\n",
       "      <td>262.390526</td>\n",
       "      <td>2.596187</td>\n",
       "      <td>942.830734</td>\n",
       "      <td>0.226459</td>\n",
       "      <td>0.059099</td>\n",
       "      <td>93.658615</td>\n",
       "      <td>-39.877816</td>\n",
       "      <td>3.571098</td>\n",
       "      <td>5159.549509</td>\n",
       "      <td>0.145003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age    duration  campaign       pdays  previous  \\\n",
       "education                                                                    \n",
       "Basic                42.163910  263.043874  2.559498  974.877967  0.141053   \n",
       "high.school          37.998213  260.886810  2.568576  964.358382  0.185917   \n",
       "illiterate           48.500000  276.777778  2.277778  943.833333  0.111111   \n",
       "professional.course  40.080107  252.533855  2.586115  960.765974  0.163075   \n",
       "university.degree    38.879191  253.223373  2.563527  951.807692  0.192390   \n",
       "unknown              43.481225  262.390526  2.596187  942.830734  0.226459   \n",
       "\n",
       "                     emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "education                                                                     \n",
       "Basic                    0.191329       93.639933     -40.927595   3.729654   \n",
       "high.school              0.032937       93.584857     -40.940641   3.556157   \n",
       "illiterate              -0.133333       93.317333     -39.950000   3.516556   \n",
       "professional.course      0.173012       93.569864     -40.124108   3.710457   \n",
       "university.degree       -0.028090       93.493466     -39.975805   3.529663   \n",
       "unknown                  0.059099       93.658615     -39.877816   3.571098   \n",
       "\n",
       "                     nr_employed         y  \n",
       "education                                   \n",
       "Basic                5172.014113  0.087029  \n",
       "high.school          5164.994735  0.108355  \n",
       "illiterate           5171.777778  0.222222  \n",
       "professional.course  5170.155979  0.113485  \n",
       "university.degree    5163.226298  0.137245  \n",
       "unknown              5159.549509  0.145003  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('education').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对分类型变量进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '数量')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.job,df.y).plot(kind='bar')\n",
    "plt.title('购买理财产品和工作岗位分布')\n",
    "plt.xlabel('Job')\n",
    "plt.ylabel('数量')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到不同的职业购买理财产品的数量是不一样的，admin,blue-collar,technician等职业购买的数量较多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y             0     1\n",
      "marital              \n",
      "divorced   4136   476\n",
      "married   22396  2532\n",
      "single     9948  1620\n",
      "unknown      68    12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '客户比例')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(df.marital,df.y)\n",
    "print(table)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('购买理财产品和婚姻状况的堆积图')\n",
    "plt.xlabel('Marital Status(婚姻状态)')\n",
    "plt.ylabel('客户比例')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "婚姻状态似乎和是否购买理财产品没有太大的关联"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y                        0     1\n",
      "education                       \n",
      "Basic                11424  1089\n",
      "high.school           8484  1031\n",
      "illiterate              14     4\n",
      "professional.course   4648   595\n",
      "university.degree    10498  1670\n",
      "unknown               1480   251\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '客户比例')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(df.education,df.y)\n",
    "print(table)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('购买理财产品和文化程度的堆积图')\n",
    "plt.xlabel('Education')\n",
    "plt.ylabel('客户比例')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "文化程度中illiterate的购买比例较高,但是购买人数相对较少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y               0     1\n",
      "day_of_week            \n",
      "fri          6981   846\n",
      "mon          7667   847\n",
      "thu          7578  1045\n",
      "tue          7137   953\n",
      "wed          7185   949\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '数量')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(df.day_of_week,df.y))\n",
    "pd.crosstab(df.day_of_week,df.y).plot(kind='bar')\n",
    "plt.title('购买理财产品和星期的分布') \n",
    "plt.xlabel('星期')\n",
    "plt.ylabel('数量')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "星期似乎和购买理财产品没有太大的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y          0    1\n",
      "month            \n",
      "apr     2093  539\n",
      "aug     5523  655\n",
      "dec       93   89\n",
      "jul     6525  649\n",
      "jun     4759  559\n",
      "mar      270  276\n",
      "may    12883  886\n",
      "nov     3685  416\n",
      "oct      403  315\n",
      "sep      314  256\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '数量')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(df.month,df.y))\n",
    "pd.crosstab(df.month,df.y).plot(kind='bar')\n",
    "plt.title('购买理财产品和月份的分布')\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('数量')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "月份似乎和购买理财产品有些关联"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '频率')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.hist()\n",
    "plt.title('年龄直方图')\n",
    "plt.xlabel('年龄(Age)')\n",
    "plt.ylabel('频率')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分接受电话访问的客户的年龄分布在30-40岁之间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y                0     1\n",
      "poutcome                \n",
      "failure       3647   605\n",
      "nonexistent  32422  3141\n",
      "success        479   894\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '数量')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(df.poutcome,df.y))\n",
    "pd.crosstab(df.poutcome,df.y).plot(kind='bar')\n",
    "plt.title('购买理财产品和上一次营销活动的结果分布')\n",
    "plt.xlabel('上一次营销活动的结果(Poutcome)')\n",
    "plt.ylabel('数量')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上一次营销活动的结果(Poutcome)似乎和是否购买理财产品有较强的相关性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对分类型变量进行one-hot编码\n",
    "\n",
    "下面我们要对数据集中的分类型变量进行one-hot编码，首先我们查看一下数据集中所有变量的信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp_var_rate      41188 non-null float64\n",
      "cons_price_idx    41188 non-null float64\n",
      "cons_conf_idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr_employed       41188 non-null float64\n",
      "y                 41188 non-null int64\n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到所有的object变量就是我们需要转换的分类型变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age' 'duration' 'campaign' 'pdays' 'previous' 'emp_var_rate'\n",
      " 'cons_price_idx' 'cons_conf_idx' 'euribor3m' 'nr_employed' 'y'\n",
      " 'job_admin.' 'job_blue-collar' 'job_entrepreneur' 'job_housemaid'\n",
      " 'job_management' 'job_retired' 'job_self-employed' 'job_services'\n",
      " 'job_student' 'job_technician' 'job_unemployed' 'job_unknown'\n",
      " 'marital_divorced' 'marital_married' 'marital_single' 'marital_unknown'\n",
      " 'education_Basic' 'education_high.school' 'education_illiterate'\n",
      " 'education_professional.course' 'education_university.degree'\n",
      " 'education_unknown' 'default_no' 'default_unknown' 'default_yes'\n",
      " 'housing_no' 'housing_unknown' 'housing_yes' 'loan_no' 'loan_unknown'\n",
      " 'loan_yes' 'contact_cellular' 'contact_telephone' 'month_apr' 'month_aug'\n",
      " 'month_dec' 'month_jul' 'month_jun' 'month_mar' 'month_may' 'month_nov'\n",
      " 'month_oct' 'month_sep' 'day_of_week_fri' 'day_of_week_mon'\n",
      " 'day_of_week_thu' 'day_of_week_tue' 'day_of_week_wed' 'poutcome_failure'\n",
      " 'poutcome_nonexistent' 'poutcome_success']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>...</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_of_week_fri</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>339</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  duration  campaign  pdays  previous  emp_var_rate  cons_price_idx  \\\n",
       "0   44       210         1    999         0           1.4          93.444   \n",
       "1   53       138         1    999         0          -0.1          93.200   \n",
       "2   28       339         3      6         2          -1.7          94.055   \n",
       "3   39       185         2    999         0          -1.8          93.075   \n",
       "4   55       137         1      3         1          -2.9          92.201   \n",
       "\n",
       "   cons_conf_idx  euribor3m  nr_employed  ...  month_oct  month_sep  \\\n",
       "0          -36.1      4.963       5228.1  ...          0          0   \n",
       "1          -42.0      4.021       5195.8  ...          0          0   \n",
       "2          -39.8      0.729       4991.6  ...          0          0   \n",
       "3          -47.1      1.405       5099.1  ...          0          0   \n",
       "4          -31.4      0.869       5076.2  ...          0          0   \n",
       "\n",
       "   day_of_week_fri  day_of_week_mon  day_of_week_thu  day_of_week_tue  \\\n",
       "0                0                0                1                0   \n",
       "1                1                0                0                0   \n",
       "2                0                0                1                0   \n",
       "3                1                0                0                0   \n",
       "4                1                0                0                0   \n",
       "\n",
       "   day_of_week_wed  poutcome_failure  poutcome_nonexistent  poutcome_success  \n",
       "0                0                 0                     1                 0  \n",
       "1                0                 0                     1                 0  \n",
       "2                0                 0                     0                 1  \n",
       "3                0                 0                     1                 0  \n",
       "4                0                 0                     0                 1  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(df[var], prefix=var)\n",
    "    df=df.join(cat_list)\n",
    "    \n",
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "df_vars=df.columns.values.tolist()\n",
    "to_keep=[i for i in df_vars if i not in cat_vars]\n",
    "df_final=df[to_keep]\n",
    "print(df_final.columns.values)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 利用SMOTE算法进行过采用\n",
    "\n",
    "\n",
    "SMOTE（Synthetic Minority Oversampling Technique），合成少数类过采样技术．它是基于随机过采样算法的一种改进方案，由于随机过采样采取简单复制样本的策略来增加少数类样本，这样容易产生模型过拟合的问题，即使得模型学习到的信息过于特别(Specific)而不够泛化(General)，SMOTE算法的基本思想是对少数类样本进行分析并根据少数类样本人工合成新样本添加到数据集中。\n",
    "\n",
    "对于不平衡类的问题，我们一般采样上采样(过采样)或下采样(负采样）策略是解决类别不平衡问题的基本方法之一。上采样即增加少数类样本的数量，下采样即减少多数类样本以获取相对平衡的数据集。大家还可以参考这篇博客(https://blog.csdn.net/nlpuser/article/details/81265614 )\n",
    "\n",
    "想进一步了解SMOTE可以参考这篇论文(https://arxiv.org/pdf/1106.1813.pdf ),这里我们将使用python的imblearn库中的SMOTE算法(http://imbalanced-learn.org/en/stable/over_sampling.html#smote-variants )来实现过采样。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先我们从数据集中分离出特征和标签，然后我们通过随机抽样创建训练集合测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_final.loc[:, df_final.columns != 'y']\n",
    "y = df_final.loc[:, df_final.columns == 'y']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "columns = X_train.columns"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们利用SMOTE算法对训练集进行过采样，采样的结果将使正样本合负样本的数量达到平衡。\n",
    "\n",
    "算法流程如下。\n",
    "\n",
    "(1)对于少数类中每一个样本x，以欧氏距离为标准计算它到少数类样本集中所有样本的距离，得到其k近邻。\n",
    "\n",
    "(2)根据样本不平衡比例设置一个采样比例以确定采样倍率N，对于每一个少数类样本x，从其k近邻中随机选择若干个样本，假设选择的近邻为xn。\n",
    "\n",
    "(3)对于每一个随机选出的近邻xn，分别与原样本按照如下的公式构建新的样本。\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "过采样之前训练集中正负样本的比例分布:\n",
      "0    25567\n",
      "1     3264\n",
      "Name: y, dtype: int64\n",
      "0    0.886789\n",
      "1    0.113211\n",
      "Name: y, dtype: float64\n",
      "-------------------------------------------\n",
      "过采样之后训练集中正负样本的比例分布:\n",
      "1    25567\n",
      "0    25567\n",
      "Name: y, dtype: int64\n",
      "1    0.5\n",
      "0    0.5\n",
      "Name: y, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('过采样之前训练集中正负样本的比例分布:')\n",
    "print(y_train['y'].value_counts())\n",
    "print(y_train['y'].value_counts()/len(y_train))\n",
    "os = SMOTE(random_state=0)\n",
    "os_data_X,os_data_y=os.fit_sample(X_train, y_train.values.ravel())\n",
    "os_data_X = pd.DataFrame(data=os_data_X,columns=columns )\n",
    "os_data_y= pd.DataFrame(data=os_data_y,columns=['y'])\n",
    "print('-------------------------------------------')\n",
    "print('过采样之后训练集中正负样本的比例分布:')\n",
    "print(os_data_y['y'].value_counts())\n",
    "print(os_data_y['y'].value_counts()/len(os_data_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过SMOTE算法过采样以后，我们看法正样本合负样本的数量都已经一致了，正负样本的比例达到了平衡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征筛选-递归特征消除\n",
    "\n",
    "递归特征消除(Recursive Feature Elimination,RFE)(https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html ),此算法是用来进行特征选择,RFE基于重复构建模型并选择表现最好或最差特征,并将它们放在一边，然后对剩下的特征重复这样的过程,直到所有的特征都被用完。RFE的目标是通过递归地考虑越来越小的特征集来选择特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_vars=df_final.columns.values.tolist()\n",
    "y=['y']\n",
    "X=[i for i in df_final_vars if i not in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原特征数量: 61\n",
      "\n",
      "经过模型筛选后的20个特征：\n",
      "['euribor3m', 'job_blue-collar', 'job_housemaid', 'marital_unknown', 'education_illiterate', 'default_no', 'default_unknown', 'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may', 'month_nov', 'month_oct', 'poutcome_failure', 'poutcome_success']\n",
      "\n",
      "特征排名:\n",
      "[39 36 24 41  9 12 22 34  1 32  8  1  7  1  5 31  2  4 28  3  6 10 20 19\n",
      " 15  1 14 17  1 16 18 21  1  1 42 25 38 35 30 37 33  1  1  1  1  1  1  1\n",
      "  1  1  1  1 27 29 11 26 40 23  1 13  1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear')\n",
    "\n",
    "rfe = RFE(logreg, 20)#选择20个特征\n",
    "rfe = rfe.fit(os_data_X.values, os_data_y.values.ravel())\n",
    "\n",
    "features = list(os_data_X.columns[rfe.support_])\n",
    "\n",
    "print('原特征数量:',os_data_X.shape[1])\n",
    "print()\n",
    "print('经过模型筛选后的20个特征：')\n",
    "print(features)\n",
    "print()\n",
    "print('特征排名:')\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 逻辑回归\n",
    "\n",
    "到目前为止我们有了平衡的二分类训练集,以及经过RFE筛选过的特征,接下来我们要用sklearn的逻辑回归模型来拟合我们的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=os_data_X[features]\n",
    "y=os_data_y['y']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression(solver='liblinear')\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在测试集上预测的准确率: 0.74\n"
     ]
    }
   ],
   "source": [
    "#在测试集上进行预测\n",
    "y_pred = logreg.predict(X_test)\n",
    "print('在测试集上预测的准确率: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8241fac1d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# F1 分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.80      0.75      7666\n",
      "           1       0.77      0.68      0.72      7675\n",
      "\n",
      "    accuracy                           0.74     15341\n",
      "   macro avg       0.74      0.74      0.74     15341\n",
      "weighted avg       0.74      0.74      0.74     15341\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于评估指标混淆矩阵、F1分数、ROC曲线在我之前多篇博客中都有说明，大家也可以参考这篇(https://blog.csdn.net/weixin_42608414/article/details/88344263)\n",
    "\n",
    "\n",
    "\n",
    "# 总结\n",
    "\n",
    "今天我们学习了在二分类问题中如何对数据进行分析，对于分类型变量我们可以使用堆积图和柱状图来进行分析，对于数值型变量我们可以用直方图来进行分析，对于分类型变量我们还需要进行one-hot编码。对于one-hot编码也有好多种方法，今天只使用了其中的一种，但今天使用的这种one-hot方法是有点缺陷的，缺陷在于它无法适应新数据，之所以适应它是因为它写起来比较方便，便于演示。今天我们还学习了两个重要的技术:SMOTE和RFE,前者是用来平衡二分类数据集的，它通过过采样的方法来增加较小分类的样本数量，从而使正负样本数量可以达到平衡，后者是通过递归特征消除的方法来从特征集中提炼有价值的特征。最后我们通过sklearn的逻辑回归模型来拟合训练集，并使用了混淆矩阵，F1分数，ROC曲线等评估指标来评估我们的模型，好了，今天就到这吧，祝大家中秋节快乐!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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